Tag: Simulation

Advanced Analytics | Analytics
Christian Goßler 0
Lenin und der Rote Rapper im Internet of Ticks (IoT5)

„… Internet, Internet, ich hör‘ hier immer Internet. Sag’n Se‘ ma‘, ganz richtig ist das nicht!“ Der Service-Manager errötet nach seinem Rap. Lenin schwankt zwischen Belustigung und bolschewistischem Ingrimm: Stellt der Rote Rapper seine Erfolge im Internet of Things infrage? Der Rapper fährt fort: „Denn diese Daten, die Sie verbraten,

Learn SAS | Programming Tips
Rick Wicklin 0
Data-driven simulation

In a large simulation study, it can be convenient to have a "control file" that contains the parameters for the study. My recent article about how to simulate multivariate normal clusters demonstrates a simple example of this technique. The simulation in that article uses an input data set that contains

Programming Tips
Rick Wicklin 0
Random segments and broken sticks

A classical problem in elementary probability asks for the expected lengths of line segments that result from randomly selecting k points along a segment of unit length. It is both fun and instructive to simulate such problems. This article uses simulation in the SAS/IML language to estimate solutions to the

Learn SAS | Programming Tips
Rick Wicklin 0
Simulate lognormal data in SAS

A SAS customer asked how to simulate data from a three-parameter lognormal distribution as specified in the PROC UNIVARIATE documentation. In particular, he wanted to incorporate a threshold parameter into the simulation. Simulating lognormal data is easy if you remember an important fact: if X is lognormally distributed, then Y=log(X)

Rick Wicklin 0
The contaminated normal distribution

How can you generate data that contains outliers in a simulation study? The contaminated normal distribution is a simple but useful distribution you can use to simulate outliers. The distribution is easy to explain and understand, and it is also easy to implement in SAS. What is a contaminated normal

Rick Wicklin 0
Sampling variation in small random samples

Somewhere in my past I encountered a panel of histograms for small random samples of normal data. I can't remember the source, but it might have been from John Tukey or William Cleveland. The point of the panel was to emphasize that (because of sampling variation) a small random sample

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